IPAC2018 - Table of Session: THYGBE (MC6 Orals)

Paper	Title	Page
THYGBE1	Applying Artificial Intelligence to Accelerators	2925
	A. Scheinker, R.W. Garnett, D. Rees LANL, Los Alamos, New Mexico, USA D.K. Bohler SLAC, Menlo Park, California, USA A.L. Edelen, S.V. Milton CSU, Fort Collins, Colorado, USA
	Particle accelerators are being designed and operated over a wide range of complex beam phase space distributions. For example, the Linac Coherent Light Source (LCLS) upgrade, LCLS-II, is considering complex schemes such as two-color operation [1], while the plasma wake field acceleration facility for advanced accelerator experimental tests (FACET) upgrade, FACET-II, is planning on providing custom tailored current profiles [2]. Because of uncertainty due to limited diagnostics and time varying performance, such as thermal drifts, as well as collective effects and the complex coupling of large numbers of components, it is impossible to use simple look up tables for parameter settings in order to quickly switch between widely varying operating ranges. Several forms of artificial intelligence are currently being investigated in order to enable accelerators to quickly and automatically re-adjust component settings without human intervention. In this work we discuss recent progress in applying neural networks and adaptive feedback algorithms to enable automatic accelerator tuning and optimization. [1] A. A. Lutman et al., Nat. Photonics 10.11, 745 (2016). [2] V. Yakimenko et al., IPAC2016, Busan, Korea, 2016.
	Slides THYGBE1 [14.256 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THYGBE1
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THYGBE2	Results and Discussion of Recent Applications of Neural Network-Based Approaches to the Modeling and Control of Particle Accelerators
	A.L. Edelen CSU, Fort Collins, Colorado, USA S. Biedron University of New Mexico, Albuquerque, USA D.L. Bowring, B.E. Chase, D.R. Edstrom, J. Steimel Fermilab, Batavia, Illinois, USA J.P. Edelen RadiaSoft LLC, Boulder, Colorado, USA P.J.M. van der Slot Mesa+, Enschede, The Netherlands
	Here we highlight several examples from our work in applying neural network-based modeling and control techniques to particle accelerator systems, through a combination of simulation and experimental studies. We also discuss where the specific approaches used fit into the state of the art in deep learning for control, including limitations of the present state of the art (for example in efficiently dealing with noisy, time-varying, many-parameter systems, like those found in accelerators). We will also briefly clarify some of the terminology/taxonomy of artificial intelligence, and describe how the neural network approaches used here relate to other classes of algorithms that are familiar to the accelerator community. The particle accelerator applications discussed include resonant frequency control of Fermilab's PIP-II RFQ, fast switching between beam parameters in a compact THz FEL, modeling of the FAST low energy beamline at Fermilab, temperature control for the FAST RF gun, and trajectory control for the Jefferson Laboratory FEL.
	Slides THYGBE2 [37.657 MB]
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THYGBE3	RF Controls for High-Q Cavities for the LCLS-II	2929
	C. Serrano, K.S. Campbell, L.R. Doolittle, G. Huang, A. Ratti LBNL, Berkeley, California, USA R. Bachimanchi, C. Hovater JLab, Newport News, Virginia, USA A.L. Benwell, M. Boyes, G.W. Brown, D. Cha, G. Dalit, J.A. Diaz Cruz, J. Jones, R.S. Kelly, A. McCollough SLAC, Menlo Park, California, USA B.E. Chase, E. Cullerton, J. Einstein-Curtis, J.P. Holzbauer, D.W. Klepec, Y.M. Pischalnikov, W. Schappert Fermilab, Batavia, Illinois, USA L.R. Dalesio, M.A. Davidsaver Osprey DCS LLC, Ocean City, USA
	Funding: This work was supported by the LCLS-II Project and the U.S. Department of Energy, Contract n. DE-AC02-76SF00515. The SLAC National Accelerator Laboratory is building LCLS-II, a new 4 GeV CW superconducting (SCRF) Linac as a major upgrade of the existing LCLS. The LCLS-II Low-Level Radio Frequency (LLRF) collaboration is a multi-lab effort within the Department of Energy (DOE) accelerator complex. The necessity of high longitudinal beam stability of LCLS-II imposes tight amplitude and phase stability requirements on the LLRF system (up to 0.01% in amplitude and 0.01° in phase RMS). This is the first time such requirements are expected of superconducting cavities operating in continuous-wave (CW) mode. Initial measurements on the Cryomodule test stands at partner labs have shown that the early production units are able to meet the extrapolated hardware requirements to achieve such levels of performance. A large effort is currently underway for system integration, Experimental Physics and Industrial Control System (EPICS) controls, transfer of knowledge from the partner labs to SLAC and the production and testing of 76 racks of LLRF equipment.
	Slides THYGBE3 [14.383 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THYGBE3
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THYGBE4	Early Phase 2 Results of LumiBelle2 for the SuperKEKB Electron Ring	2934
	S. Di Carlo, P. Bambade, D. Jehanno, V. Kubytskyi, C.G. Pang, Y. Peinaud, C. Rimbault LAL, Orsay, France
	We report on the early SuperKEKB Phase 2 operations of the fast luminosity monitor (LumiBelle2 project). Fast luminosity monitoring is required by the dithering feedback system, which is used to stabilize the beam in the presence of horizontal vibrations. In this report, we focus on the operations related to the electron side of LumiBelle2. Diamond sensors are located 30 meters downstream of the IP, just above, beside, and below the electron beam pipe. During early Phase 2, the sensors are used to measure the background, arising from beam-gas scattering. We present the hardware design, the detection algorithm, and the analysis of the background measurements taken up-to-date. The results are then compared with a detailed simulation of the background, in order to well understand the physical processes involved. The simulation is performed using SAD for generation and tracking purposes, while Geant4 is used to calculate the energy deposition in the diamond sensors.
	Slides THYGBE4 [3.091 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THYGBE4
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Paper

Title

Page

Applying Artificial Intelligence to Accelerators

2925

A. Scheinker, R.W. Garnett, D. Rees
LANL, Los Alamos, New Mexico, USA
D.K. Bohler
SLAC, Menlo Park, California, USA
A.L. Edelen, S.V. Milton
CSU, Fort Collins, Colorado, USA

Particle accelerators are being designed and operated over a wide range of complex beam phase space distributions. For example, the Linac Coherent Light Source (LCLS) upgrade, LCLS-II, is considering complex schemes such as two-color operation [1], while the plasma wake field acceleration facility for advanced accelerator experimental tests (FACET) upgrade, FACET-II, is planning on providing custom tailored current profiles [2]. Because of uncertainty due to limited diagnostics and time varying performance, such as thermal drifts, as well as collective effects and the complex coupling of large numbers of components, it is impossible to use simple look up tables for parameter settings in order to quickly switch between widely varying operating ranges. Several forms of artificial intelligence are currently being investigated in order to enable accelerators to quickly and automatically re-adjust component settings without human intervention. In this work we discuss recent progress in applying neural networks and adaptive feedback algorithms to enable automatic accelerator tuning and optimization.
[1] A. A. Lutman et al., Nat. Photonics 10.11, 745 (2016).
[2] V. Yakimenko et al., IPAC2016, Busan, Korea, 2016.

Slides THYGBE1 [14.256 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THYGBE1

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THYGBE2

Results and Discussion of Recent Applications of Neural Network-Based Approaches to the Modeling and Control of Particle Accelerators

A.L. Edelen
CSU, Fort Collins, Colorado, USA
S. Biedron
University of New Mexico, Albuquerque, USA
D.L. Bowring, B.E. Chase, D.R. Edstrom, J. Steimel
Fermilab, Batavia, Illinois, USA
J.P. Edelen
RadiaSoft LLC, Boulder, Colorado, USA
P.J.M. van der Slot
Mesa+, Enschede, The Netherlands

Here we highlight several examples from our work in applying neural network-based modeling and control techniques to particle accelerator systems, through a combination of simulation and experimental studies. We also discuss where the specific approaches used fit into the state of the art in deep learning for control, including limitations of the present state of the art (for example in efficiently dealing with noisy, time-varying, many-parameter systems, like those found in accelerators). We will also briefly clarify some of the terminology/taxonomy of artificial intelligence, and describe how the neural network approaches used here relate to other classes of algorithms that are familiar to the accelerator community. The particle accelerator applications discussed include resonant frequency control of Fermilab's PIP-II RFQ, fast switching between beam parameters in a compact THz FEL, modeling of the FAST low energy beamline at Fermilab, temperature control for the FAST RF gun, and trajectory control for the Jefferson Laboratory FEL.

Slides THYGBE2 [37.657 MB]

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THYGBE3

RF Controls for High-Q Cavities for the LCLS-II

2929

C. Serrano, K.S. Campbell, L.R. Doolittle, G. Huang, A. Ratti
LBNL, Berkeley, California, USA
R. Bachimanchi, C. Hovater
JLab, Newport News, Virginia, USA
A.L. Benwell, M. Boyes, G.W. Brown, D. Cha, G. Dalit, J.A. Diaz Cruz, J. Jones, R.S. Kelly, A. McCollough
SLAC, Menlo Park, California, USA
B.E. Chase, E. Cullerton, J. Einstein-Curtis, J.P. Holzbauer, D.W. Klepec, Y.M. Pischalnikov, W. Schappert
Fermilab, Batavia, Illinois, USA
L.R. Dalesio, M.A. Davidsaver
Osprey DCS LLC, Ocean City, USA

Funding: This work was supported by the LCLS-II Project and the U.S. Department of Energy, Contract n. DE-AC02-76SF00515.
The SLAC National Accelerator Laboratory is building LCLS-II, a new 4 GeV CW superconducting (SCRF) Linac as a major upgrade of the existing LCLS. The LCLS-II Low-Level Radio Frequency (LLRF) collaboration is a multi-lab effort within the Department of Energy (DOE) accelerator complex. The necessity of high longitudinal beam stability of LCLS-II imposes tight amplitude and phase stability requirements on the LLRF system (up to 0.01% in amplitude and 0.01° in phase RMS). This is the first time such requirements are expected of superconducting cavities operating in continuous-wave (CW) mode. Initial measurements on the Cryomodule test stands at partner labs have shown that the early production units are able to meet the extrapolated hardware requirements to achieve such levels of performance. A large effort is currently underway for system integration, Experimental Physics and Industrial Control System (EPICS) controls, transfer of knowledge from the partner labs to SLAC and the production and testing of 76 racks of LLRF equipment.

Slides THYGBE3 [14.383 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THYGBE3

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THYGBE4

Early Phase 2 Results of LumiBelle2 for the SuperKEKB Electron Ring

2934

S. Di Carlo, P. Bambade, D. Jehanno, V. Kubytskyi, C.G. Pang, Y. Peinaud, C. Rimbault
LAL, Orsay, France

We report on the early SuperKEKB Phase 2 operations of the fast luminosity monitor (LumiBelle2 project). Fast luminosity monitoring is required by the dithering feedback system, which is used to stabilize the beam in the presence of horizontal vibrations. In this report, we focus on the operations related to the electron side of LumiBelle2. Diamond sensors are located 30 meters downstream of the IP, just above, beside, and below the electron beam pipe. During early Phase 2, the sensors are used to measure the background, arising from beam-gas scattering. We present the hardware design, the detection algorithm, and the analysis of the background measurements taken up-to-date. The results are then compared with a detailed simulation of the background, in order to well understand the physical processes involved. The simulation is performed using SAD for generation and tracking purposes, while Geant4 is used to calculate the energy deposition in the diamond sensors.

Slides THYGBE4 [3.091 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THYGBE4

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